| Nematus: a toolkit for neural machine translation R Sennrich, O Firat, K Cho, A Birch, B Haddow, J Hitschler, ... arXiv preprint arXiv:1703.04357, 2017 | 452 | 2017 |
| Findings of the IWSLT 2022 Evaluation Campaign. A Anastasopoulos, L Barrault, L Bentivogli, MZ Boito, O Bojar, R Cattoni, ... Proceedings of the 19th International Conference on Spoken Language …, 2022 | 105 | 2022 |
| Predicting Target Language CCG Supertags Improves Neural Machine Translation M Nadejde, S Reddy, R Sennrich, T Dwojak, M Junczys-Dowmunt, ... Second Conference on Machine Translation 1, 68--79, 2017 | 99* | 2017 |
| Findings of the iwslt 2023 evaluation campaign M Agarwal, S Agarwal, A Anastasopoulos, L Bentivogli, O Bojar, C Borg, ... Association for Computational Linguistics, 2023 | 38 | 2023 |
| Edinburgh’s syntax-based systems at wmt 2015 P Williams, R Sennrich, M Nǎdejde, M Huck, P Koehn Proceedings of the Tenth Workshop on Statistical Machine Translation, 199-209, 2015 | 37 | 2015 |
| Enabling Robust Grammatical Error Correction in New Domains: Datasets, Metrics, and Analyses C Napoles, M Nadejde, J Tetreault Transactions of the Association for Computational Linguistics 7, 551-566, 2019 | 36 | 2019 |
| Edinburgh’s Statistical Machine Translation Systems for WMT16 P Williams, R Sennrich, M Nadejde, M Huck, B Haddow, O Bojar Proceedings of the First Conference on Machine Translation, Berlin, Germany …, 2016 | 32 | 2016 |
| MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation A Currey, M Nădejde, R Pappagari, M Mayer, S Lauly, X Niu, B Hsu, ... EMNLP 2022, 2022 | 25 | 2022 |
| Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1 M Nadejde, J Tetreault The 5th Workshop on Noisy User-generated Text (W-NUT), 2019 | 23 | 2019 |
| The feasibility of HMEANT as a human MT evaluation metric A Birch, B Haddow, U Germann, M Nǎdejde, C Buck, P Koehn Proceedings of the Eighth Workshop on Statistical Machine Translation, 52-61, 2013 | 19 | 2013 |
| Edinburgh’s syntax-based machine translation systems M Nǎdejde, P Williams, P Koehn Proceedings of the Eighth Workshop on Statistical Machine Translation, 170-176, 2013 | 18 | 2013 |
| Eu-bridge mt: Combined machine translation M Freitag, S Peitz, J Wuebker, H Ney, M Huck, R Sennrich, N Durrani, ... Proceedings of the Ninth Workshop on Statistical Machine Translation, 105-113, 2014 | 16 | 2014 |
| CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality M Nădejde, A Currey, B Hsu, X Niu, M Federico, G Dinu NAACL, 2022 | 15 | 2022 |
| Sockeye 3: Fast neural machine translation with pytorch F Hieber, M Denkowski, T Domhan, BD Barros, CD Ye, X Niu, C Hoang, ... arXiv preprint arXiv:2207.05851, 2022 | 14 | 2022 |
| A baseline revisited: Pushing the limits of multi-segment models for context-aware translation S Majumder, S Lauly, M Nadejde, M Federico, G Dinu arXiv preprint arXiv:2210.10906, 2022 | 13 | 2022 |
| Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation M Nadejde, A Birch, P Koehn Proceedings of the First Conference on Machine Translation, 32--42, 2016 | 8 | 2016 |
| RAMP: Retrieval and attribute-marking enhanced prompting for attribute-controlled translation G Sarti, PM Htut, X Niu, B Hsu, A Currey, G Dinu, M Nadejde ACL 2023, 2023 | 5 | 2023 |
| A Neural Verb Lexicon Model with Source-side Syntactic Context for String-to-Tree Machine Translation M Nadejde, A Birch, P Koehn Proceedings of the International Workshop on Spoken Language Translation (IWSLT), 2016 | 2 | 2016 |
| Syntactic and semantic features for statistical and neural machine translation M Nadejde The University of Edinburgh, 2018 | 1 | 2018 |
| M3T: A new benchmark dataset for multi-modal document-level machine translation B Hsu, X Liu, H Li, Y Fujinuma, M Nadejde, X Niu, Y Kittenplon, R Litman, ... arXiv preprint arXiv:2406.08255, 2024 | | 2024 |
